We are formalizing the problem of IML not only as a repertoire of methods but by proposing a chain-like process of procedures, inspired by the notation of [1]}. While direct modelling of causal relations is still in a preliminary stage [2]}, IML methods are applied to render correlation human-understandable as an intermediary step via either inherently interpretable models or post-training explanatory methods [3]}, [4]}. fig1 shows how the human \(H\)  from the class of stakeholders, \(\mathcal {H}\)  then is interpreting and utilizing this machine-based explanation \(E\)  to make a causal statement \(C\) . \(C\)  can incorporate causal statements about, e.g., fairness, reliability, or transparency, which also can be wrong.
